Currently, the main method for visualize tsibble objects is with the autoplot function from fabletools / ggplot2 packages to create auto-plots of tsibble objects.

Let’s load and transform the natural gas consumption in the US into tsibble object and plot it:

library(tsibble)
library(fabletools)
library(dplyr)

naturalgas_path <- paste(rprojroot::find_rstudio_root_file(), "data", "NATURALGAS.csv", sep = "/")

us_gas <- read.csv(naturalgas_path, stringsAsFactors = FALSE) %>%
  setNames(c("date", "y")) %>%
  mutate(date = yearmonth(as.Date(date))) %>%
  as_tsibble(index = "date")

us_gas %>% autoplot(.vars = y)

As the autoplot function exported from the ggplot2 package, we can customize the plot and add additional layers using any of the ggplot2 package functionality:

library(ggplot2)

us_gas %>% autoplot(.vars = y) + 
  xlab("Source: U.S. Bureau of Transportation Statistics, Natural Gas Consumption [NATURALGAS]") + 
  ylab("Billion Cubic Feet") + 
  ggtitle("US Natural Gas Consumption (Not Seasonally Adjusted)")

Last but not least, we can convert the plot into a plotly object using the ggplotly function from the plotly package:

library(plotly)

p <- us_gas %>% autoplot(.vars = y) + 
  xlab("Source: U.S. Bureau of Transportation Statistics, Natural Gas Consumption [NATURALGAS]") + 
  ylab("Billion Cubic Feet") + 
  ggtitle("US Natural Gas Consumption (Not Seasonally Adjusted)")


ggplotly(p)

Plotting multiple time series objects

data("tourism")

nsw_tourism <- tourism %>% filter(State == "New South Wales", Purpose == "Holiday")


nsw_tourism <- tourism %>% filter(Region == "Sydney")

head(nsw_tourism)
## # A tsibble: 6 x 5 [1Q]
## # Key:       Region, State, Purpose [1]
##   Quarter Region State           Purpose  Trips
##     <qtr> <chr>  <chr>           <chr>    <dbl>
## 1 1998 Q1 Sydney New South Wales Business  525.
## 2 1998 Q2 Sydney New South Wales Business  545.
## 3 1998 Q3 Sydney New South Wales Business  691.
## 4 1998 Q4 Sydney New South Wales Business  599.
## 5 1999 Q1 Sydney New South Wales Business  444.
## 6 1999 Q2 Sydney New South Wales Business  794.
key_data(nsw_tourism)
## # A tibble: 4 x 4
##   Region State           Purpose        .rows
## * <chr>  <chr>           <chr>    <list<int>>
## 1 Sydney New South Wales Business        [80]
## 2 Sydney New South Wales Holiday         [80]
## 3 Sydney New South Wales Other           [80]
## 4 Sydney New South Wales Visiting        [80]
nsw_tourism %>% autoplot()